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Phylodynamic Inference across Epidemic Scales.

Erik M Volz1, Ethan Romero-Severson2, Thomas Leitner2

  • 1Department of Infectious Disease Epidemiology, Imperial College London, London, UK.

Molecular Biology and Evolution
|February 17, 2017
PubMed
Summary

A new multi-scale coalescent model accurately estimates pathogen transmission dynamics by accounting for within-host diversity and imperfect bottlenecks. This approach provides more reliable epidemiological insights for viruses like HIV-1 and Ebola virus (EBOV).

Keywords:
EbolaHIVcoalescentphylodynamics

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Area of Science:

  • Epidemiology
  • Viral Evolution
  • Computational Biology

Background:

  • Phylodynamic inference traditionally assumes direct correlation between phylogenetic tree nodes and transmission events.
  • Within-host pathogen genetic diversity and transmission bottlenecks can violate these assumptions, leading to inaccurate epidemiological estimates.
  • Existing models struggle to incorporate these complexities, potentially misrepresenting disease spread.

Purpose of the Study:

  • To develop and validate a novel multi-scale coalescent model that addresses violations of standard phylodynamic assumptions.
  • To accurately infer epidemiological dynamics by accounting for within-host diversity and imperfect transmission bottlenecks.
  • To assess the impact of within-host diversity on phylodynamic inference for HIV-1 and Ebola virus (EBOV).

Main Methods:

  • Developed a multi-scale coalescent model incorporating nonlinear epidemiological dynamics, heterogeneous sampling, within-host diversity, and imperfect transmission bottlenecks.
  • Applied the model to HIV-1 and EBOV sequence data from outbreaks.
  • Compared results from the multi-scale model with conventional phylodynamic approaches.

Main Results:

  • The multi-scale model demonstrated greater consistency with reported diagnoses for HIV-1 compared to conventional models, which showed upward bias in infected host numbers.
  • Within-host diversity of EBOV had minimal impact on estimated infected hosts and reproduction numbers, aligning well with reported diagnoses.
  • The model enabled estimation of within-host effective population size, yielding a diversity estimate of 2Nμ=0.012 for HIV-1 p17.

Conclusions:

  • The multi-scale coalescent model offers a more robust framework for phylodynamic inference in the presence of within-host diversity and transmission bottlenecks.
  • Conventional models can produce misleading results for diseases like HIV-1 due to unaddressed within-host complexities.
  • The developed method provides valuable insights into viral population genetics and epidemiological dynamics.